In any manufacturing process, there are process machines which take multiple inputs (such as gases, materials, power, etc.), provide local environments (such as pressure, temperature, etc.), and change either the shape or some set of properties of the operant material(s). The output is a specific arrangement of new properties (e.g., thickness, index of refraction, stress, geometry, etc.) on the operant material, and are called herein either the output, the product, or the goal.
In order to predict the output of a machine (hereinafter, whenever the term “machine” is used, it should be understood that it may also refer to a process) for a specific set of inputs, there is a need to provide a model of the machine.
One type of a model that describes the behavior and the operation of a machine is the physical model. A physical model comprises a set of mathematical functions and formulas based on scientific understanding of the machine and process. Physical models often contain some calibration constants which are related to some physical characteristic of the process but for which the first principle knowledge is either limited or lacking. These constants may be adjusted to allow the model to properly simulate the process.
Other types of models, like data based models, or others only try to fit experimental data from the machine to simple functions. These models are constructed by means of carrying out experiments and/or accumulating a large amount of raw data relating to the characteristics of the output product versus different sets of inputs. They are generally used in cases where the physical model is not known. These models treat the machine as a “black box”, and try to predict the properties of the output product versus the inputs, based on experimental results using simple formulas with undetermined constants. Constants in the simple functions are evaluated by fitting the functions through the data. Thus, the constants also allow the model to simulate the process, but unlike the physical model, they are not connected to any physical characteristic. For this reason, physical models are usually more accurate. A major point to be noted here is that the simple functions used in these data based models have no relation to the actual behavior of the physical mechanisms present in the process and hence cannot generally be used in the present invention, as discussed hereinafter. However, in cases when these models fortuitously or by design do properly account for the physical mechanisms of the process, then such models could also be used in the present invention. Such latter cases are within the scope of this invention. When the term ‘physical model’ is used in this disclosure, it is understood that this includes any model, which properly follows the behavior of physical mechanisms.
Important applications of accurate models of a process include process optimization (i.e., finding a setup which optimizes some cost function of the output, see, for example, Israeli Patent Application 134,380) and certain aspects of failure analysis including health monitoring, and diagnostics. This invention addresses the latter by describing the methods of determining the root cause or causes of changes in the output properties.
Nearly every process can experience change in the output properties with time. These changes are caused by some drift in one or more of the inputs, or from some other changes in the properties of the process machine and its environment with no change by the operator of the input settings. Each of these possible mechanisms results in a more or less unique pattern of variation in the machine operation and the output properties versus time.
It has been found by the inventors that if one has the great advantage of having an accurate physical model of a process, it is possible to use the model to determine which mechanism or mechanisms is the cause of the change. This is a major component of process machine health monitoring and diagnostics. The present invention embodies the application of accurate models to such determinations.
Changes in output properties in a process will eventually trigger some sort of alarm. This leads generally to requiring changes in the inputs or, worse, cessation of the process for maintenance. Changes to inputs may allow for a return of output properties to desired levels, but the underlying cause for the drift is not addressed. Cessation of the process for maintenance typically restores the machine to proper working order, but frequently a more comprehensive and expensive refurbishing is performed because the underlying cause of the drift is not understood. If such an understanding is available, a much more effective and simpler remedy would result in more machine availability, closer adherence to desired output properties, reduction in production cost and much better control of the process.
The present invention shows that by taking the advantage of having an accurate physical model of a process, and by having a small amount of measured data, the cause/s for changes to output properties can be identified and controlled.
The essence of the invention is that an accurate model can provide extra information unavailable by any other source, of monitoring changes to the process machine and diagnosing these changes. This includes information regarding possible states of the machine for any measured output, derivatives of the output quantities with respect to input quantities, and possible paths of output change with a change in one of the inputs, to name a few. Several methods for diagnosis or cause analysis can then be envisioned, which, along with data routinely collected during production can assist in maintaining product quality, reducing repair time of machines, early detection of problems, diagnosis of which mechanism or controller has drifted out of spec, and general health monitoring.